Ready to learn about analyzing data for an e-commerce store? In this video, we'll discuss some of the basics of analytics, including how to set goals, evaluate metrics, and measure success. You've learned about analytics in a previous course, but now we're going to consider how analytics applies specifically to e-commerce. Analyzing data is one of the best ways for an e-commerce store to figure out what's working and what isn't. That's true whether a business is a major online retailer or a small business just starting out. With the insights gained from analytics, an e-commerce store can determine which sales and marketing tactics are the most effective. They can also use analytics to better understand customer behavior. All of these insights help a business discover which tactics provide the best results. These are the areas where a business should invest their time and money because they'll deliver the most return on their investment. The data a company analyzes can come from multiple sources. For example, you've already learned about Google Analytics. Many e-commerce platforms include their own built-in analytics as well, plus e-mail marketing, social media, and other tools sometimes include built-in analytics. Companies can also gather data from other sources, such as customer surveys, A/B testing, and heat maps, which you'll learn about later. How does an e-commerce store measure success using data? They start by setting goals for each area of their business. As you learned in a previous course, these goals should be SMART, which stands for specific, measurable, attainable, relevant, and time-bound. Each of these SMART goals contributes towards achieving the ultimate goal of success in e-commerce. Part of setting goals for an e-commerce store is deciding which metrics to track to measure the success of a business or marketing goal. As you learned in a previous course, these metrics are a company's Key Performance Indicators, or KPIs. A KPI is a measurement used to gauge how successful a business is in its effort to reach a business or marketing goal. The metrics that a company monitors might be different depending on their specific goals and how much data they've accumulated over time. Newer e-commerce stores will find it helpful to compare the results of their metrics quarter-over-quarter. A quarter is a three-month time period based on a company's financial calendar. Each year includes four quarters. Comparing results to the previous time period helps a company determine whether their metrics are improving over time. Companies that sell high-priced products, such as diamond jewelry or upscale furniture, need to be aware that the conversion process will take longer. Customers might need more time to research their options and make a decision. That's when it's helpful to track micro conversions, which you learned about in a previous course. Micro conversions indicate that a potential customer is moving towards a completed purchase transaction. Stores that have been around for at least a couple of years will use both quarter-over- quarter and year-over- year comparisons to measure growth and revenue. When an e-commerce store has been around for a couple of years or longer, they have access to more data and longer-lasting relationships with their customers. This means the company can track metrics over a longer period of time. They can also focus more heavily on metrics that relate to customer loyalty. Let's use an example to demonstrate how data relates to a company's goals. Imagine an online store that sells office supplies. The store sets a goal to increase their conversion rate by 1 percent in the next six months. To measure the results, they'll track the conversion rate metric in their analytics tool. They might also go deeper into the conversion rate by separating mobile and desktop visitors to find out how they behave differently on the site. If mobile visitors convert at a lower rate, for example, the company can work on improving the mobile experience. Another tool the company might use to gather data is a heat map. A heat map demonstrates how visitors interact with the website. This can help an e-commerce store make improvements to their website. For example, do customers get stuck on the promotion code field during checkout and end up leaving the checkout process to search for promotions? The company can use this data to make improvements to the checkout process. They might make the promotion code field less prominent, to reduce friction in the checkout process, and improve conversion rates. Now that you have a better idea of how to set goals and analyze data, let's consider how analytics applies to the entire marketing funnel. Starting from when a customer first discovers a brand to when they become a loyal brand advocate. E-commerce analytics helps companies discover important information about their customers, such as where their traffic comes from, and which channels attract the most visitors and sales. Companies can also learn information about their customers' geographic location, interests, behavior, and other data. As customers move through the marketing funnel, they arrive at the checkout process. Here, companies can use analytics to measure sales and shopping cart metrics such as conversion rate, average order value, and cart abandonment rate. We'll go deeper into those metrics later. Finally, analytics helps e-commerce stores measure customer loyalty. The customer lifetime value is a helpful metric for this because it estimates the total amount of money that a customer is expected to spend with the business over their lifetime. The higher the number, the better, because retaining existing customers is more cost-effective than acquiring new ones. For some e-commerce stores, the customer lifetime value might be based on a subscription service, such as meal kits, or a repeat purchase, such as water filters. For others, it might be based on a one-time purchase, such as a musical instrument. Analytics also helps companies measure brand advocacy, which is the strongest form of customer loyalty. Brand advocacy measures the number of customers who promote a brand through word-of-mouth marketing. The Net Promoter Score, or NPS, is a metric that measures brand advocacy by asking how loyal customers are to a company. The NPS data is gathered through a survey that asks customers how likely they would be to recommend the company to a friend or colleague. Customers then rate the company on a scale from 0 to 10. The results help companies form an overall picture of how customers view their brand. To recap, you've learned why analyzing data for e-commerce is important and how to set goals and measure success. You've also learned how analytics relates to the marketing funnel. Coming up, you'll learn more about e-commerce analytics. Meet you again soon.
